Multivariate Predictive Approach Research Articles

The Motivation for ISO 9000 Certification: A Multivariate Predictive Approach

The Motivation for ISO 9000 Certification: A Multivariate Predictive Approach

A-231 Can Embedded Performance Validity Measures Predict Performance on a Stand-Alone Test?

Abstract Objective: Determine the extent to which a group of embedded performance validity tests (PVT) predict performance on a stand-alone test; and examine the classification accuracy of embedded PVTs. Method: Participants were 102 adult patients who underwent neuropsychological evaluation (M age = 31.85, SD = 11.79; M education = 13.82, SD = 2.50; M FSIQ = 104.72). Patients were administered the Medical Symptom Validity Test (MSVT), and 10 embedded PVT scores were extracted from their neuropsychological profiles. Logistic regression determined the extent to which scores on embedded PVTs predicted MSVT designation as credible or noncredible. Classification accuracy also was calculated for the embedded PVTs, both individually and based on number of “failures.” Results: A final regression model (Chi-square (6) = 20.06, p = .003, Nagelkerke R Square = .42) was 38% sensitive, 97% specific, and correctly predicted credible patients (negative predictive accuracy = 95%) more often than noncredible patients (positive predictive accuracy = 50%). Several embedded PVTs had comparable classification accuracy. Conservatively defining noncredible performance as failure on at least three embedded PVTs produced the best combination of sensitivity (50%) and specificity (86%), and was associated with a false positive rate of 14%. Conclusions: Embedded PVTs correctly predict credible and noncredible performance on the MSVT at a high level. Although a multivariate predictive approach may have slightly more utility than examining embedded PVT scores separately, several individual PVTs have comparable accuracy. It remains unclear which approach is superior, and more research is needed.

Using Multibeam Backscatter Data to Investigate Sediment‐Acoustic Relationships

AbstractSediment properties are known to influence acoustic backscatter intensity. This sediment‐acoustic relationship has been investigated previously through using physical geoacoustic models and empirical methods and found to be complex and nonlinear. Here we employ a robust machine‐learning statistical model (random forest decision tree) to investigate the most likely nonlinear sediment‐backscatter relationships. The analysis uses colocated sediment and acoustic backscatter data (collected from a 300‐kHz multibeam sonar system) for 564 locations in four different areas on the Australian margin. Seven sediment grain size properties (%gravel, %sand, %mud, mean grain size, sorting, skewness, and kurtosis) were used to predict the acoustic backscatter responses at individual incidence angles. The modeling results demonstrate the effectiveness of this multivariate predictive approach for the investigation of sediment‐acoustic relationship. Thus, we find that for incidence angles between 1° and 41°, the sediment variables explain around 70% of variance in the backscatter intensity. Sediment mud content was found to be the most important sediment variable in the model and has a significant negative relationship with backscatter intensity. Mean grain size was the second ranked sediment variable and found to have a positive relationship with backscatter intensity. The results also show that sediment mud content plays a key role in sorting‐backscatter and sand‐backscatter relationships. Using only two sediment properties, mud content and mean grain size, together it was possible to largely explain the sediment‐acoustic relationship. The strongest backscatter return occurred with medium sediment mud content and large mean grain sizes (or muddy coarse sand).

Measuring Burden of Unhealthy Behaviours Using a Multivariable Predictive Approach: Life Expectancy Lost in Canada Attributable to Smoking, Alcohol, Physical Inactivity, and Diet.

BackgroundBehaviours such as smoking, poor diet, physical inactivity, and unhealthy alcohol consumption are leading risk factors for death. We assessed the Canadian burden attributable to these behaviours by developing, validating, and applying a multivariable predictive model for risk of all-cause death.MethodsA predictive algorithm for 5 y risk of death—the Mortality Population Risk Tool (MPoRT)—was developed and validated using the 2001 to 2008 Canadian Community Health Surveys. There were approximately 1 million person-years of follow-up and 9,900 deaths in the development and validation datasets. After validation, MPoRT was used to predict future mortality and estimate the burden of smoking, alcohol, physical inactivity, and poor diet in the presence of sociodemographic and other risk factors using the 2010 national survey (approximately 90,000 respondents). Canadian period life tables were generated using predicted risk of death from MPoRT. The burden of behavioural risk factors attributable to life expectancy was estimated using hazard ratios from the MPoRT risk model.FindingsThe MPoRT 5 y mortality risk algorithms were discriminating (C-statistic: males 0.874 [95% CI: 0.867–0.881]; females 0.875 [0.868–0.882]) and well calibrated in all 58 predefined subgroups. Discrimination was maintained or improved in the validation cohorts. For the 2010 Canadian population, unhealthy behaviour attributable life expectancy lost was 6.0 years for both men and women (for men 95% CI: 5.8 to 6.3 for women 5.8 to 6.2). The Canadian life expectancy associated with health behaviour recommendations was 17.9 years (95% CI: 17.7 to 18.1) greater for people with the most favourable risk profile compared to those with the least favourable risk profile (88.2 years versus 70.3 years). Smoking, by itself, was associated with 32% to 39% of the difference in life expectancy across social groups (by education achieved or neighbourhood deprivation).ConclusionsMultivariable predictive algorithms such as MPoRT can be used to assess health burdens for sociodemographic groups or for small changes in population exposure to risks, thereby addressing some limitations of more commonly used measurement approaches. Unhealthy behaviours have a substantial collective burden on the life expectancy of the Canadian population.

Interaction between spawning habitat and coastally steered circulation regulate larval fish retention in a large shallow temperate bay

Larval retention plays a fundamental role in the persistence of coastal fish assemblages. Here, we examine larval fish distribution and abundance patterns in Palma Bay, a large (∼20 km) wind-driven microtidal bay in the southern coast of Mallorca (Spain, NW Mediterranean Sea). Larval fish assemblage structure in the bay were analyzed during July 2010 and interpreted in the context of the observed circulation patterns, adult habitat distribution and spawning traits. Acoustic Doppler Current Profiler (ADCP) observations showed the presence of retentive flow patterns in the middle of the bay enhancing local larval accumulation and self-recruitment. In consequence, larval abundances were higher in this central part of the bay (∼40 m depth, mean abundance 607.6 ± 383 ind. 10 m−2) than along the coastal fringe (<35 m 113.7 ± 91 ind. 10 m−2). Also, a multivariate predictive approach based on Redundancy Analysis (RDA) revealed differences between the larval fish assemblages in areas inside the bay, constituted by small pelagic and benthopelagic taxa (gobids, Chromis chromis and Serranus hepatus) and offshore larvae, mostly from meso and large pelagic fish. These larval fish assemblages were structured according to depth variations and zooplankton abundance, and remained relatively unmixed because of the circulation patterns in the mouth of the bay that uncouple its dynamics from alongshelf circulation. Even larvae of typically pelagic species that spawn close to the coast (Sardinella aurita, Auxis rochei) were associated with the retentive effect of the bay. Our study highlights the important role of coastal bays in the regulation of coastal fish population dynamics and as hotspots for the maintenance of diversity in the Mediterranean Sea.

Multivariable Predictive Functional Control of an Autoclave

This paper presents the predictive functional control of an autoclave, which is designed, tested and compared in uni- and multivariable manners. The control of the autoclave is based on our previously developed mathematical model for an autoclave, where we dealt with the heat-transfer and pressure-changing processes. First, we presented the principles of the predictive control algorithm, which are easy to understand. Next, the basic principles of predictive control were extended to a multivariable manner, so we presented the control law of the multivariable predictive algorithm. Furthermore, we depicted the suggested tuning rules for both control algorithms, which normally give satisfactory results, considering the trade-off between robustness and performance. We implemented both predictive algorithms in the linearized and simplified autoclave model, where we applied faster tuning rules due to the need for faster closed-loop responses. For the comparison we also designed and applied a classical compensating PI controller. The results show the superior performance of the multivariable predictive approach. All three algorithms rise similarly quickly, but then the PI and the simple predictive controller slowly approach the desired value due to slower tuning, because of the very noisy manipulated variable. The interactions in the autoclave model are not so strong, so by the interactions influence rejection, both predictive algorithms show similar performances, while the PI approach performs much worse. The multivariable predictive approach also proves its superior performance by the interactions influence rejection when controlling the processes with stronger interactions. We can conclude that the autoclave should be controlled as one multivariable process using a multivariable predictive functional approach.